def test(self):
xla_device = xm.xla_device()
kdata = [_gen_tensor(2, 3), _gen_tensor(3, 4)]
kdata.append([_gen_tensor(2, 5), _gen_tensor(3, 6)])
data = dict()
data[_gen_tensor(2, 2)] = tuple(kdata)
data[_gen_tensor(2, 4)] = set([12.0, _gen_tensor(3, 7)])
data['ABC'] = _gen_tensor(4, 3)
def select_fn(v):
return type(v) == torch.Tensor
def convert_fn(tensors):
devices = [str(xla_device)] * len(tensors)
return torch_xla._XLAC._xla_tensors_from_aten(tensors, devices)
def check_fn(v):
if select_fn(v):
return xm.is_xla_tensor(v)
elif isinstance(v, (list, tuple, set)):
for x in v:
if not check_fn(x):
return False
elif isinstance(v, dict):
for k, x in v.items():
if not check_fn(k) or not check_fn(x):
return False
return True
xla_data = xm.ToXlaTensorArena(convert_fn, select_fn).transform(data)
self.assertTrue(check_fn(xla_data))
def _send_data_to(self, data, device):
def convert_fn(tensors):
devices = [str(device)] * len(tensors)
return torch_xla._XLAC._xla_tensors_from_aten(tensors, devices)
def select_fn(v):
return type(v) == torch.Tensor
return xm.ToXlaTensorArena(convert_fn, select_fn).transform(data)
def _rewrite_data(path, data, save_tensors):
def convert_fn(tensors):
torch_xla._XLAC._xla_sync_multi(tensors,
devices=[],
wait=True,
sync_xla_data=True)
rewritten_tensors = []
for i, t in enumerate(tensors):
if save_tensors:
torch.save(t.cpu(), _get_tensor_file(path, i))
rewritten_tensors.append(TensorReference(i))
return rewritten_tensors
def select_fn(v):
return type(v) == torch.Tensor and xm.is_xla_tensor(v)
os.mkdir(path)
return xm.ToXlaTensorArena(convert_fn, select_fn).transform(data)
def load(path):
"""Loads data previously saved with the `save()` API.
Args:
path (str): The path passed to the `save()` API.
Returns:
The loaded data.
"""
ref_data = torch.load(path)
tensor_folder = _get_tensors_folder(path)
def convert_fn(tensors):
rewritten_tensors = []
for t in tensors:
rewritten_tensors.append(
torch.load(_get_tensor_file(tensor_folder, t.tid)))
return rewritten_tensors
def select_fn(v):
return type(v) == TensorReference
return xm.ToXlaTensorArena(convert_fn, select_fn).transform(ref_data)
